# The Embedding Layer

In *Chapter 4, Deep Learning for Text – Embeddings*, we discussed that we can't feed text directly into a neural network, and therefore need good representations. We discussed that embeddings (low-dimensional, dense vectors) are a great way of representing text. To pass the embeddings into the neural network's layers, we need to employ the embedding layer.

The functionality of the embedding layer is two-fold:

- For any input term, perform a lookup and return its word embedding/vector
- During training, learn these word embeddings

The part about looking up is straightforward – the word embeddings are stored as a matrix of the `V × D`

dimensionality, where `V`

is the vocabulary size (the number of unique terms considered) and `D`

is the length/dimensionality of each vector. The following figure illustrates the embedding layer. The input term, "`life`

", is passed to the embedding layer, which performs a lookup and returns...